def __init__(self, params, datadict, is_eval=False):
self.params = params
self.datadict = datadict
self.is_eval = is_eval
self.max_length = params['max_length'] if 'max_length' in params and not is_eval else 1000
self.apply_spec_augment = True if params['spec_augment'] and not self.is_eval else False
if self.apply_spec_augment:
logging.info('Apply SpecAugment!')
self.spec_augment_config = params['spec_augment_config']
logging.info('Config: %s' % ' '.join([key+':'+str(value) for key, value in self.spec_augment_config.items()]))
self.unit2idx = load_vocab(params['vocab'])
self.target_dict = {}
for text_file in self.datadict['text']:
with open(text_file, 'r', encoding='utf-8') as f:
for line in f:
parts = line.strip().split()
utt_id = parts[0]
tokens = self.encode(parts[1:])
if len(tokens) > self.max_length: continue
self.target_dict[utt_id] = tokens
self.feat_list = []
for feat_file in self.datadict['feat']:
with open(feat_file, 'r', encoding='utf-8') as f:
for line in f:
utt_id, feat_path = line.strip().split()
if utt_id not in self.target_dict: continue
self.feat_list.append([utt_id, feat_path])
if 'utt2spk' in self.datadict:
self.apply_cmvn = True
assert 'cmvn' in self.datadict
self.utt2spk = {}
for utt2spk in self.datadict['utt2spk']:
with open(utt2spk, 'r') as f:
for line in f:
uttid, spkid = line.strip().split()
self.utt2spk[uttid] = spkid
self.cmvn = {}
for cmvn in self.datadict['cmvn']:
with open(cmvn, 'r') as f:
for line in f:
spkid, path = line.strip().split()
self.cmvn[spkid] = path
logging.info('Apply CMVN!')
else:
self.apply_cmvn = False
def main(args):
checkpoint = torch.load(args.load_model)
if 'params' in checkpoint:
params = checkpoint['params']
else:
assert os.path.isfile(args.config), 'please specify a configure file.'
with open(args.config, 'r') as f:
params = yaml.load(f)
params['data']['shuffle'] = False
params['data']['spec_argument'] = False
params['data']['short_first'] = False
params['data']['batch_size'] = args.batch_size
model = Transformer(params['model'])
model.load_state_dict(checkpoint['model'])
print('Load pre-trained model from %s' % args.load_model)
model.eval()
if args.ngpu > 0:
model.cuda()
char2unit = load_vocab(params['data']['vocab'])
unit2char = {i: c for c, i in char2unit.items()}
recognizer = TransformerRecognizer(model,
unit2char=unit2char,
beam_width=args.beam_width,
max_len=args.max_len,
penalty=args.penalty,
lamda=args.lamda,
ngpu=args.ngpu)
# inputs_length: [len]
inputs, inputs_length = calc_fbank(args.file, params['data'])
if args.ngpu > 0:
inputs = inputs.cuda()
inputs_length = inputs_length.cuda()
preds = recognizer.recognize(inputs, inputs_length)
print('preds: {}'.format(preds[0].replace(' ', '')))
def __init__(self, params, datadict, is_eval=False):
self.params = params
self.datadict = datadict
self.is_eval = is_eval
self.apply_spec_augment = True if params[
'spec_augment'] and not self.is_eval else False
if self.apply_spec_augment:
logging.info('Apply SpecAugment!')
self.spec_augment_config = params['spec_augment_config']
logging.info('Config: %s' % ' '.join([
key + ':' + str(value)
for key, value in self.spec_augment_config.items()
]))
self.unit2idx = load_vocab(params['vocab'])
with open(self.datadict['json'], 'r') as f:
self.utts = [(k, v) for k, v in json.load(f)['utts'].items()]
def main(args):
checkpoint = torch.load(args.load_model)
if 'params' in checkpoint:
params = checkpoint['params']
else:
assert os.path.isfile(args.config), 'please specify a configure file.'
with open(args.config, 'r') as f:
params = yaml.load(f)
params['data']['shuffle'] = False
params['data']['spec_argument'] = False
params['data']['short_first'] = False
params['data']['batch_size'] = args.batch_size
expdir = os.path.join('egs', params['data']['name'], 'exp',
params['train']['save_name'])
if args.suffix is None:
decode_dir = os.path.join(expdir, 'decode_%s' % args.decode_set)
else:
decode_dir = os.path.join(
expdir, 'decode_%s_%s' % (args.decode_set, args.suffix))
if not os.path.exists(decode_dir):
os.makedirs(decode_dir)
model = Transformer(params['model'])
model.load_state_dict(checkpoint['model'])
print('Load pre-trained model from %s' % args.load_model)
model.eval()
if args.ngpu > 0:
model.cuda()
char2unit = load_vocab(params['data']['vocab'])
unit2char = {i: c for c, i in char2unit.items()}
dataset = AudioDataset(params['data'], args.decode_set)
data_loader = FeatureLoader(dataset)
recognizer = TransformerRecognizer(model,
unit2char=unit2char,
beam_width=args.beam_width,
max_len=args.max_len,
penalty=args.penalty,
lamda=args.lamda,
ngpu=args.ngpu)
totals = len(dataset)
batch_size = params['data']['batch_size']
writer = open(os.path.join(decode_dir, 'predict.txt'), 'w')
for step, (utt_id, batch) in enumerate(data_loader.loader):
if args.ngpu > 0:
inputs = batch['inputs'].cuda()
inputs_length = batch['inputs_length'].cuda()
preds = recognizer.recognize(inputs, inputs_length)
targets = batch['targets']
targets_length = batch['targets_length']
for b in range(len(preds)):
n = step * batch_size + b
truth = ' '.join(
[unit2char[i.item()] for i in targets[b][1:targets_length[b]]])
print('[%d / %d ] %s - pred : %s' %
(n, totals, utt_id[b], preds[b]))
print('[%d / %d ] %s - truth: %s' % (n, totals, utt_id[b], truth))
writer.write(utt_id[b] + ' ' + preds[b] + '\n')
writer.close()
def __init__(self, params, datadict, is_eval=False):
self.params = params
self.datadict= datadict
self.is_eval = is_eval
self.apply_spec_augment = params['spec_augment'] if not self.is_eval else False
logger.info('[Online-Reader] Read the feature extracted online!')
self.normalization = params['normalization']
self.feature_extractor = params['feature_extractor'] if 'feature_extractor' in params else 'torchaudio'
assert self.feature_extractor in ['torchaudio', 'python_speech_feature', 'ta', 'psf']
logger.info('Utilize %s to extract feature from wav.' % self.feature_extractor)
if self.normalization:
logger.info('Apply Feature Normalization!')
if 'global_cmvn' in params:
self.global_mean = torch.from_numpy(np.load(params['global_cmvn'] + '.mean.npy'))
self.global_std = torch.from_numpy(np.load(params['global_cmvn'] + '.std.npy'))
logger.info('Load global mean and std vector from files')
self.apply_global_cmvn = True
else:
self.apply_global_cmvn = False
if self.apply_spec_augment and not self.is_eval:
self.spec_augment_config = params['spec_augment_config']
logger.info('Apply SpecAugment!')
logger.info('Config: %s' % ' '.join([key+':'+str(value) for key, value in self.spec_augment_config.items()]))
if 'gaussian_noise' in params and not self.is_eval:
self.gaussian_noise = params['gaussian_noise']
if self.gaussian_noise > 0.0:
logger.info('Apply Gaussian Noise with std: %f.' % self.gaussian_noise)
else:
self.gaussian_noise = 0.0
# if 'speed_perturb' in params and not self.is_eval:
# self.apply_speed_perturb = params['speed_perturb']
# if self.apply_speed_perturb: logger.info('Apply Speed Perturb during the training!')
# else:
# self.apply_speed_perturb = False
if 'volume_perturb' in params and not self.is_eval:
self.apply_volume_perturb = params['volume_perturb']
if self.apply_volume_perturb: logger.info('Apply Volume Perturb during the training!')
else:
self.apply_volume_perturb = False
self.unit2idx = load_vocab(params['vocab'])
self.targets_dict = {}
for text_file in self.datadict['text']:
with open(text_file, 'r', encoding='utf-8') as t:
for line in t:
parts = line.strip().split()
utt_id = parts[0]
label = []
for c in parts[1:]:
label.append(self.unit2idx[c] if c in self.unit2idx else self.unit2idx[UNK_TOKEN])
self.targets_dict[utt_id] = label
self.file_list = []
for feat_file in self.datadict['feat']:
with open(feat_file, 'r', encoding='utf-8') as fid:
for line in fid:
idx, path = line.strip().split()
self.file_list.append([idx, path])
assert len(self.file_list) <= len(self.targets_dict)
def main(args):
checkpoint = torch.load(args.load_model)
if 'params' in checkpoint:
params = checkpoint['params']
else:
assert os.path.isfile(args.config), 'please specify a configure file.'
with open(args.config, 'r') as f:
params = yaml.load(f)
params['data']['shuffle'] = False
params['data']['spec_augment'] = False
params['data']['short_first'] = False
params['data']['batch_size'] = args.batch_size
expdir = os.path.join('egs', params['data']['name'], 'exp',
params['train']['save_name'])
decoder_set_name = 'decode_%s' % args.decode_set
if args.load_language_model is not None:
decoder_set_name += '_lm_lmw%.2f' % args.lm_weight
if args.suffix is not None:
decoder_set_name += '_%s' % args.suffix
decode_dir = os.path.join(expdir, decoder_set_name)
if not os.path.exists(decode_dir):
os.makedirs(decode_dir)
model = Transformer(params['model'])
model.load_state_dict(checkpoint['model'])
print('Load pre-trained model from %s' % args.load_model)
model.eval()
if args.ngpu > 0:
model.cuda()
if args.load_language_model is not None:
lm_chkpt = torch.load(args.load_language_model)
lm = TransformerLanguageModel(lm_chkpt['params']['model'])
lm.load_state_dict(lm_chkpt['model'])
lm.eval()
if args.ngpu > 0: lm.cuda()
print('Load pre-trained transformer language model from %s' %
args.load_language_model)
else:
lm = None
char2unit = load_vocab(params['data']['vocab'])
unit2char = {i: c for c, i in char2unit.items()}
data_loader = FeatureLoader(params, args.decode_set, is_eval=True)
recognizer = TransformerRecognizer(model,
lm=lm,
lm_weight=args.lm_weight,
unit2char=unit2char,
beam_width=args.beam_width,
max_len=args.max_len,
penalty=args.penalty,
lamda=args.lamda,
ngpu=args.ngpu)
totals = len(data_loader.dataset)
batch_size = params['data']['batch_size']
writer = open(os.path.join(decode_dir, 'predict.txt'), 'w')
writerRef = open(os.path.join(decode_dir, 'reference.txt'), 'w')
for step, (utt_id, batch) in enumerate(data_loader.loader):
if args.ngpu > 0:
inputs = batch['inputs'].cuda()
inputs_length = batch['inputs_length'].cuda()
else:
inputs = batch['inputs']
inputs_length = batch['inputs_length']
preds = recognizer.recognize(inputs, inputs_length)
targets = batch['targets']
targets_length = batch['targets_length']
for b in range(len(preds)):
n = step * batch_size + b
truth = ' '.join([
unit2char[i.item()]
for i in targets[b][1:targets_length[b] + 1]
])
print('[%d / %d ] %s - pred : %s' %
(n, totals, utt_id[b], preds[b]))
print('[%d / %d ] %s - truth: %s' % (n, totals, utt_id[b], truth))
if utt_id[b][7] == '1':
newpred = preds[b] + " (" + 'S1000' + "-" + utt_id[b] + ")"
newtruth = truth + " (" + 'S1000' + "-" + utt_id[b] + ")"
elif utt_id[b][7] == '2':
newpred = preds[b] + " (" + 'S2000' + "-" + utt_id[b] + ")"
newtruth = truth + " (" + 'S2000' + "-" + utt_id[b] + ")"
elif utt_id[b][0] == 'O':
newpred = preds[b] + " (" + 'O' + "-" + utt_id[b] + ")"
newtruth = truth + " (" + 'O' + "-" + utt_id[b] + ")"
else:
newpred = preds[b] + " (" + utt_id[b][6:11] + "-" + utt_id[
b] + ")"
newtruth = truth + " (" + utt_id[b][6:11] + "-" + utt_id[
b] + ")"
writer.write(newpred + '\n')
writerRef.write(newtruth + '\n')
writer.close()
writerRef.close()
